The growing popularity of electric vehicles (EVs) presents a mounting challenge for power system engineers when it comes to integrating them into residential distribution systems. In this paper, a communication-free E...
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Coronavirus(COVID-19 or SARS-CoV-2)is a novel viral infection that started in December 2019 and has erupted rapidly in more than 150 *** rapid spread of COVID-19 has caused a global health emergency and resulted in go...
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Coronavirus(COVID-19 or SARS-CoV-2)is a novel viral infection that started in December 2019 and has erupted rapidly in more than 150 *** rapid spread of COVID-19 has caused a global health emergency and resulted in governments imposing lock-downs to stop its *** is a signifi-cant increase in the number of patients infected,resulting in a lack of test resources and kits in most *** overcome this panicked state of affairs,researchers are looking forward to some effective solutions to overcome this situa-tion:one of the most common and effective methods is to examine the X-radiation(X-rays)and computed tomography(CT)images for detection of ***-ever,this method burdens the radiologist to examine each ***,to reduce the burden on the radiologist,an effective,robust and reliable detection system has been developed,which may assist the radiologist and medical specia-list in effective detecting of *** proposed a deep learning approach that uses readily available chest radio-graphs(chest X-rays)to diagnose COVID-19 *** proposed approach applied transfer learning to the Deep Convolutional Neural Network(DCNN)model,Inception-v4,for the automatic detection of COVID-19 infection from chest X-rays *** dataset used in this study contains 1504 chest X-ray images,504 images of COVID-19 infection,and 1000 normal images obtained from publicly available medical *** results showed that the proposed approach detected COVID-19 infection with an overall accuracy of 99.63%.
This research aims to develop a sophisticated floor evenness measurement system for the construction industry, addressing the subjectivity, inefficiency, and labour-intensive nature of current methodologies. A mobile ...
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Considering the carbon footprint of rapidly evolving quantum systems and technologies, it is essential to develop energy efficient and sustainable next generation quantum communication systems. Simultaneous Lightwave ...
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Matched Field Processing (MFP) and its derivative Matched Mode Processing (MMP) estimates similarity between in-situ measurement and the model-based replica to identify source locations from an array of received signa...
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The rapid expansion of the Internet of Things (IoT) has revolutionized modern life, offering unparalleled automation and seamless interconnectivity between devices, often operating without user intervention. However, ...
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ISBN:
(数字)9798331523268
ISBN:
(纸本)9798331523275
The rapid expansion of the Internet of Things (IoT) has revolutionized modern life, offering unparalleled automation and seamless interconnectivity between devices, often operating without user intervention. However, this convenience comes with a significant trade-off: increased susceptibility of IoT devices to cyberattacks, which can result in severe consequences if not promptly addressed. To tackle this pressing challenge, our study proposes innovative strategies powered by machine learning algorithms, achieving an exceptional 99.97% detection accuracy and a 0.0% false positive rate. Leveraging the Bot-IoT dataset for evaluation, our approach demonstrates marked improvements over existing detection methodologies. Furthermore, its adaptability to diverse IoT applications underscores its potential as a transformative advancement in IoT security.
Connected and Autonomous Vehicles (CAVs) are becoming a promising solution in Intelligent Transportation Systems (ITS). Despite these advancements, vehicles still use a Controller Area Network (CAN) bus system to comm...
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The world steel industry is highly dependent on the use of electric arc furnaces (EAFs). The application of the electric arc phenomenon causes many power quality (PQ) problems, such as harmonics or voltage flickering....
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A brain tumor is an abnormal growth of cells in the brain, critical for diagnosis and treatment. The rising incidence of brain tumors highlights the need for early identification, as their diversity and complexity mak...
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Existing works in federated learning (FL) often assume either full client or uniformly distributed client participation. However, in reality, some clients may never participate in FL training (aka incomplete client pa...
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Existing works in federated learning (FL) often assume either full client or uniformly distributed client participation. However, in reality, some clients may never participate in FL training (aka incomplete client participation) due to various system heterogeneity factors. A popular solution is the server-assisted federated learning (SA-FL) framework, where the server uses an auxiliary dataset. Despite empirical evidence of SA-FL's effectiveness in addressing incomplete client participation, theoretical understanding of SA-FL is lacking. Furthermore, the effects of incomplete client participation in conventional FL are poorly understood. This motivates us to rigorously investigate SA-FL. Toward this end, we first show that conventional FL is not PAC-learnable under incomplete client participation in the worst case. Then, we show that the PAC-learnability of FL with incomplete client participation can indeed be revived by SA-FL, which theoretically justifies the use of SA-FL for the first time. Lastly, to provide practical guidance for SA-FL training under incomplete client participation, we propose the SAFARI (server-assisted federated averaging) algorithm that enjoys the same linear convergence speedup guarantees as classic FL with ideal client participation assumptions, offering the first SA-FL algorithm with convergence guarantee. Extensive experiments on different datasets show SAFARI significantly improves the performance under incomplete client participation. Copyright 2024 by the author(s)
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